基于信息融合和GA-BP的煤矿瓦斯浓度预测方法研究

Research on Coal Mine Gas Concentration Prediction Method Based on Information Fusion and GA-BP

瓦斯浓度是煤矿安全生产的重要指标,采用合理的模型预测瓦斯浓度可提前采取安全保障措施。将CO浓度、温度、风速和甲烷浓度作为监测数据,设计传感器布局方案,以GA算法优化BP神经网络模型的权值和阈值,提高瓦斯浓度预测模型的准确率。以采样数据的后10组为测试数据,试验结果显示,GA-BP神经网络的预测误差低于5%,可满足使用需求。

Gas concentration is an important indicator of coal mine safety production. Using a reasonable model to predict gas concentration can take safety measures in advance. CO concentration, temperature,wind speed and methane concentration are used as monitoring data, the sensor layout scheme is designed, and the GA algorithm is used to optimize the weights and thresholds of the BP neural network model to improve the accuracy of the gas concentration prediction model. Taking the last 10groups of sampled data as test data, the test results show that the prediction error of the GA-BP neural network is less than 5%, which can meet the needs of use.